Skill UI

A rigorous, security-focused code review process designed for Pull Requests (PRs), commits, and diffs. It moves beyond general reviews by focusing intensely on high-risk areas such as authentication, cryptography, external calls, and value transfer. The methodology is evidence-based, requiring detailed attack scenario modeling and generating comprehensive, auditable markdown reports to identify overlooked vulnerabilities and regressions.

This skill provides a comprehensive framework for the continuous self-improvement of makepad-skills. It outlines automatic skill triggering via hooks, context-aware skill routing, and a structured process for capturing new patterns, error solutions, and best practices. It ensures the entire skill library remains robust and up-to-date throughout the development lifecycle.

This guide outlines a systematic process for transforming any recurring technical pattern, complex debugging solution, or workflow into a standalone, portable skill. It details the multi-step extraction workflow, including pattern identification, structured documentation generation (SKILL.md), and quality control checks to ensure the skill can be reused across different projects.

This skill guides the process of auditing and correcting critical HTML metadata elements, ensuring optimal performance for search engines and social media sharing. It covers page titles, meta descriptions, canonical URLs, Open Graph tags, Twitter cards, favicons, JSON-LD structured data, and robots directives. Use this when implementing or refining SEO metadata, fixing broken social previews, or setting up structured data.

This skill fetches unread GitHub Pull Requests (PRs) that require review from a specified team or were opened by a team member. It is ideal for users who need to check their team's review queue, find PRs needing attention, or automate the process of reviewing open code submissions without manually browsing GitHub notifications. Requires GitHub CLI authentication.

This utility is designed to build reusable, composable command-line scripts for interacting with the Hugging Face API and the `hf` command line tool. It enables complex data workflows by supporting chaining, piping, and intermediate processing across models, datasets, and repositories, streamlining the automation of Hub tasks.

This assistant streamlines the workflow for managing GitHub issues using the GitHub CLI. It guides users through structured processes to create, list, and view issues. When creating, it prompts for detailed information (including issue type, reproduction steps, and expected vs. actual behavior for bugs), ensuring high-quality issue reports. It also allows filtering and listing of existing issues.

This skill generates a complete, opinionated, modern full-stack Rails application boilerplate. It sets up a robust tech stack including Rails 8, PostgreSQL, React, Inertia.js, Vite, Tailwind CSS, Sidekiq, and Redis. Designed for developers who need a starting point that adheres to best practices for database structure (UUIDs, JSONB), background job processing, comprehensive testing, and modern frontend development (TSX). Ideal for bootstrapping complex, production-grade web services.

A comprehensive guide and methodology for identifying, measuring, and fixing performance bottlenecks across the entire stack. This skill covers optimizing database queries (e.g., N+1 problems, missing indexes), improving API efficiency (caching, parallel processing), and enhancing frontend performance (memoization, code splitting, image optimization). It emphasizes the critical 'Measure First' principle to ensure all optimizations are data-driven and effective.

This guide outlines best practices for creating high-quality, structured pull requests (PRs) using Git and GitHub CLI. It ensures that every PR description captures not only what changed, but also the 'why' and necessary context for reviewers, significantly improving the code review process and adherence to engineering standards.

This skill outlines a comprehensive methodology for designing, building, and deploying robust LLM-powered applications. It covers assessing task-model fit, designing modular pipelines (acquire -> prepare -> process -> parse -> render), and managing state using file systems to ensure scalability and reliability in AI systems.

A comprehensive guide and best practices for managing event handling and defining custom action patterns within Makepad applications. It covers centralized action processing (using MatchEvent), widget-to-widget communication, and implementing complex state logic, making it ideal for building robust chat clients or complex UI systems.